Tracing the path from health to disease:forwards and backwards

Positron Emission Tomography (PET) is currently underutilised and can provide even more valuable information. PET is not merely an imaging device, but a measurement device. It measures molecules and their concentration inside a living subject. And as by definition life is not a static process, PET measures how molecules travel inside our bodies and provides a direct way to measure the kinetics of biochemical reactions.There are more technological developments needed to enable dynamic imaging and kinetics in clinical practice for a range of radioactive molecules. Furthermore, quantification of biological processes using PET has several challenges related to imaging a living subject including the effects of ionising radiation. Can we minimise the radiation dose per scan which will then make it easier to justify scanning a person with multiple radioactive molecules?For example, imaging multiple molecules simultaneously will allow to evaluate pixel-by-pixel the potential performance of drugs and swiftly decide how to proceed with treatments. In addition, it can help measuring the potential crosstalk of diseases such as cancer and cardiovascular diseases, or of different organs such as brain and heart, or gut and brain axes.But more research is necessary to allow imaging two or more radioactive molecules simultaneously. Advancing PET technology can empower medicine by tracing several molecular pathways and interactions from human health to disease – forwards and backwards – and has a lot to offer for detecting and understanding disease processes and optimising precision medicine

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, ¹⁸F-FDG and ⁶⁸Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions

Generalized 3D and 4D motion compensated whole-body PET image reconstruction employing nested em deconvolution

Whole-body dynamic and parametric PET imaging has recently gained increased interest as a clinically feasible truly quantitative imaging solution for enhanced tumor detectability and treatment response monitoring in oncology. However, in comparison to static scans, dynamic PET acquisitions are longer, especially when extended to large axial field-of-view whole-body imaging, increasing the probability of voluntary (bulk) body motion. In this study we propose a generalized and novel motion-compensated PET image reconstruction (MCIR) framework to recover resolution from realistic motion-contaminated static (3D), dynamic (4D) and parametric PET images even without the need for gated acquisitions. The proposed algorithm has been designed for both single-bed and whole-body static and dynamic PET scans. It has been implemented in fully 3D space on STIR open-source platform by utilizing the concept of optimization transfer to efficiently compensate for motion at each tomographic expectation-maximization (EM) update through a nested Richardson-Lucy EM iterative deconvolution algorithm. The performance of the method, referred as nested RL-MCIR reconstruction, was evaluated on realistic 4D simulated anthropomorphic digital XCAT phantom data acquired with a clinically feasible whole-body dynamic PET protocol and contaminated with measured non-rigid motion from MRI scans of real human volunteers at multiple dynamic frames. Furthermore, in order to assess the impact of our method in whole-body PET parametric imaging, the reconstructed motion-corrected dynamic PET images were fitted with a multi-bed Patlak graphical analysis method to produce metabolic uptake rate (Ki parameter in Patlak model) images of highly quantitative value. Our quantitative Contrast-to-Noise (CNR) and noise vs. bias trade-off analysis results suggest considerable resolution enhancement in both dynamic and parametric motion-degraded whole-body PET images after applying nested RL-MCIR method, without amplification of noise

FDG PET/CT versus Bone Marrow Biopsy for Diagnosis of Bone Marrow Involvement in Non-Hodgkin Lymphoma:A Systematic Review

The management of non-Hodgkin lymphoma (NHL) patients requires the identification of bone marrow involvement (BMI) using a bone marrow biopsy (BMB), as recommended by international guidelines. Multiple studies have shown that [F-18]FDG positron emission tomography, combined with computed tomography (PET/CT), may provide important information and may detect BMI, but there is still an ongoing debate as to whether it is sensitive enough for NHL patients in order to replace or be used as a complimentary method to BMB. The objective of this article is to systematically review published studies on the performance of [F-18]FDG PET/CT in detecting BMI compared to the BMB for NHL patients. A population, intervention, comparison, and outcome (PICO) search in PubMed and Scopus databases (until 1 November 2021) was performed. A total of 41 studies, comprising 6147 NHL patients, were found to be eligible and were included in the analysis conducted in this systematic review. The sensitivity and specificity for identifying BMI in NHL patients were 73% and 90% for [F-18]FDG PET/CT and 56% and 100% for BMB. For aggressive NHL, the sensitivity and specificity to assess the BMI for the [F-18]FDG PET/CT was 77% and 94%, while for the BMB it was 58% and 100%. However, sensitivity and specificity to assess the BMI for indolent NHL for the [F-18]FDG PET/CT was 59% and 85%, while for the BMB it was superior, and equal to 94% and 100%. With regard to NHL, a [F-18]FDG PET/CT scan can only replace BMB if it is found to be positive and if patients can be categorized as having advanced staged NHL with high certainty. [F-18]FDG PET/CT might recover tumors missed by BMB, and is recommended for use as a complimentary method, even in indolent histologic subtypes of NHL

A 3D MR-acquisition scheme for nonrigid bulk motion correction in simultaneous PET-MR.

PURPOSE: Positron emission tomography (PET) is a highly sensitive medical imaging technique commonly used to detect and assess tumor lesions. Magnetic resonance imaging (MRI) provides high resolution anatomical images with different contrasts and a range of additional information important for cancer diagnosis. Recently, simultaneous PET-MR systems have been released with the promise to provide complementary information from both modalities in a single examination. Due to long scan times, subject nonrigid bulk motion, i.e., changes of the patient's position on the scanner table leading to nonrigid changes of the patient's anatomy, during data acquisition can negatively impair image quality and tracer uptake quantification. A 3D MR-acquisition scheme is proposed to detect and correct for nonrigid bulk motion in simultaneously acquired PET-MR data. METHODS: A respiratory navigated three dimensional (3D) MR-acquisition with Radial Phase Encoding (RPE) is used to obtain T1- and T2-weighted data with an isotropic resolution of 1.5 mm. Healthy volunteers are asked to move the abdomen two to three times during data acquisition resulting in overall 19 movements at arbitrary time points. The acquisition scheme is used to retrospectively reconstruct dynamic 3D MR images with different temporal resolutions. Nonrigid bulk motion is detected and corrected in this image data. A simultaneous PET acquisition is simulated and the effect of motion correction is assessed on image quality and standardized uptake values (SUV) for lesions with different diameters. RESULTS: Six respiratory gated 3D data sets with T1- and T2-weighted contrast have been obtained in healthy volunteers. All bulk motion shifts have successfully been detected and motion fields describing the transformation between the different motion states could be obtained with an accuracy of 1.71 ± 0.29 mm. The PET simulation showed errors of up to 67% in measured SUV due to bulk motion which could be reduced to less than 10% with the proposed motion compensation approach. CONCLUSIONS: A MR acquisition scheme which yields both high resolution 3D anatomical data and highly accurate nonrigid motion information without an increase in scan time is presented. The proposed method leads to a strong improvement in both MR and PET image quality and ensures an accurate assessment of tracer uptake

Integration of advanced 3D SPECT modeling into the open-source STIR framework

Purpose: The Software for Tomographic Image Reconstruction (STIR,http://stir.sourceforge.net) package is an open source object-oriented library implemented in C++. Although its modular design is suitable for reconstructing data from several modalities, it currently only supports Positron Emission Tomography (PET) data. In this work, the authors present results for Single Photon Emission Computed Tomography (SPECT) imaging. / Methods: This was achieved by the complete integration of a 3D SPECT system matrix modeling library into STIR. / Results: The authors demonstrate the flexibility of the combined software by reconstructing simulated and acquired projections from three different scanners with different iterative algorithms of STIR. / Conclusions: The extension of the open source STIR project with advanced SPECT modeling will enable the research community to study the performance of several algorithms on SPECT data, and potentially implement new algorithms by expanding the existing framework

The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer

Objectives: Measuring tumour heterogeneity by textural analysis in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) provides predictive and prognostic information but technical aspects of image processing can influence parameter measurements. We therefore tested effects of image smoothing, segmentation and quantisation on the precision of heterogeneity measurements. Methods: Sixty-four 18F-FDG PET/CT images of oesophageal cancer were processed using different Gaussian smoothing levels (2.0, 2.5, 3.0, 3.5, 4.0 mm), maximum standardised uptake value (SUVmax) segmentation thresholds (45 %, 50 %, 55 %, 60 %) and quantisation (8, 16, 32, 64, 128 bin widths). Heterogeneity parameters included grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRL), neighbourhood grey-tone difference matrix (NGTDM), grey-level size zone matrix (GLSZM) and fractal analysis methods. The concordance correlation coefficient (CCC) for the three processing variables was calculated for each heterogeneity parameter. Results: Most parameters showed poor agreement between different bin widths (CCC median 0.08, range 0.004–0.99). Segmentation and smoothing showed smaller effects on precision (segmentation: CCC median 0.82, range 0.33–0.97; smoothing: CCC median 0.99, range 0.58–0.99). Conclusions: Smoothing and segmentation have only a small effect on the precision of heterogeneity measurements in 18F-FDG PET data. However, quantisation often has larger effects, highlighting a need for further evaluation and standardisation of parameters for multicentre studies. Key points: • Heterogeneity measurement precision in 18 F-FDG PET is influenced by image processing methods. • Quantisation shows large effects on precision of heterogeneity parameters in 18 F-FDG PET/CT. • Smoothing and segmentation show comparatively smaller effects on precision of heterogeneity parameters

Estimation of Timing Resolution for Very Fast Time-Of-Flight Detectors in Monte Carlo Simulations

In PET imaging the depth of absorption in the crystal contributes to the detection time uncertainty, which impacts the time resolution of the scatter. In addition, affects the nature of the timing distribution. It was found that when Photon Travel Spread (PTS) in the crystal is the only factor affecting the timing uncertainty, in which case, a Laplace kernel might describe the measured data, more accurately. It was shown that for crystals as thin as 20 mm the RMSE of the Laplace was smaller than that of a Normal. While when PTS is combined with an addition coincidence detection resolution (CDR) then, a Normal achieves better RMSE, but with dependency on the crystal size. Results in terms of CRC, of a simulated NEMA phantom, confirmed that reconstruction using a Laplace kernel can model the data better for thicker crystals